library(dplyr)
library(sf)
library(tidyverse)
library(leaflet)
library(sf)
library(leaflet.providers)
library(RColorBrewer)
library(tigris)
Dot Density Plot
# education_url <- "https://github.com/dyerlab/ENVS-Lectures/raw/master/data/edu_attainment-shp.zip"
#
# file <- tempfile(pattern = "edu", tmpdir = ".", fileext = ".zip")
# file
#
# download.file(education_url, destfile = file)
# unzip(file, exdir = ".")
#
#
# points <- st_read("edu_attainment-shp")
setwd("~/Spring2021/GISinR/GISinR")
The working directory was changed to C:/Users/Charis/Documents/Spring2021/GISinR/GISinR inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
points <- st_read("edu_attainment-shp")
Reading layer `edu_attainment' from data source `C:\Users\Charis\Documents\Spring2021\GISinR\GISinR\edu_attainment-shp' using driver `ESRI Shapefile'
Simple feature collection with 15745 features and 1 field
geometry type: POINT
dimension: XY
bbox: xmin: -77.59891 ymin: 37.44779 xmax: -77.38568 ymax: 37.60228
geographic CRS: WGS 84
Convert education levels to factor - this allows us to use colorFactor() to create our color palette
points$education <- factor(points$education, levels = c("No HS diploma", "HS, no Bachelors", "Bachelors" , "Post-Bachelors" ))
Create color palette
factpal <- colorFactor(brewer.pal(n = 4, name = "RdBu"), points$education) #used to color categorical data
Create dot density plot
leaflet(points) %>%
addProviderTiles(providers$CartoDB.Positron)%>% #add provider tiles
addCircles(stroke = FALSE, fillOpacity = 1, #add points
color = ~factpal(education)) %>% #color based on educational attainment
addLegend(pal = factpal, values = ~education, title = "Education Attainment")%>% #add legend
addScaleBar(position = "bottomright") #add scale bar
Choropleth plot
load in the census tract data
tracts <- tracts("VA", "Richmond city") %>%
st_transform(4326)
Using FIPS code '51' for state 'VA'
Using FIPS code '760' for 'Richmond city'
Data Prep
edu_tracts <- tracts %>%
st_buffer(dist = 0) %>% # buffer by 0 to avoid error message
st_intersection(points) # intersect the point data with the tract polygons
edu_tracts %>%
group_by(NAME, education) %>% # group by tract name and education level
tally() -> tally # tally() counts the number of rows for the groups, so output is one row for every attainment level for each tract (so if a tract has all 4 attainment levels, there are 4 rows of data for that tract)
tally <- as.data.frame(tally) # converted to data.frame to be able to drop the geometry because the `pivot_wider()` function would not work the way I wanted it to when the geometry was present
tally <- tally %>%
select(NAME, education, n)%>% # do not want the geometry
pivot_wider(names_from = education, values_from = n) # pivot the table so that instead of having multiple rows per tract, you have one row per tract with a column for each attainment level
tally[is.na(tally)] <- 0 # define NA values as zero
tally
Create attainment metric for choropleth
tally <- tally %>%
mutate(edu = (((`No HS diploma` * 1) + (`HS, no Bachelors` * 2) + (`Bachelors` * 3) + (`Post-Bachelors` * 4))/
(`No HS diploma` + `HS, no Bachelors` + `Bachelors` + `Post-Bachelors`)))
# I created a column, edu, and did a weighted average for each row. So 'No HS diploma' was weighted as 1, 'HS, no Bachelors' as 2, ect.
# I used this new column as my average education attainment value to color my polygons
tally %>%
select(NAME, edu) -> tally # no longer need the individual count data
Join the education data back with the tract data
tracts %>%
left_join(tally, by = "NAME") -> tracts
Create the palette
pal <- colorNumeric(
palette = "RdBu",
domain = tracts$edu) # This time I want a continuos palatte, so I used `colorNumeric()`
Make the choropleth map
leaflet(tracts) %>%
addProviderTiles(providers$CartoDB.Positron)%>% # add provider tiles
addPolygons( fillOpacity = 1, # add tract polygons
fillColor = ~pal(edu), smoothFactor = 0.2, # fill color based on the calculated 'edu' metric
color = "grey",
opacity = 1,
weight = 1) %>%
addLegend(pal = pal, values = ~edu, title = "Education Attainment") # add legend
I did not like this legend, as it’s unclear what the numbers mean. With some googling I found a solution
labels <-c("No HS diploma (1)", "HS, no Bachelors (2)",
"Bachelors (3)" , "Post-Bachelors (4)" ) # define the labels for the legend
leaflet(tracts) %>%
addProviderTiles(providers$CartoDB.Positron)%>% # add provider tiles
addPolygons( fillOpacity = 1, # add tract polygons
fillColor = ~pal(edu), smoothFactor = 0.2, # fill color based on the calculated 'edu' metric
color = "grey",
opacity = 1,
weight = 1) %>%
addLegend(pal = pal, values = ~edu,
opacity = 1,
title = "Education Attainment",
labFormat = function(type, cuts, p) { # Here's the trick
paste0(labels)
})
That looks better, I think
Combine the maps
Final map
---
title: "Leaflet Homework Key"
author: "Charis Deadwyler"
date: "3/15/2021"
output: html_notebook
---

```{r}
library(dplyr)
library(sf)
library(tidyverse)
library(leaflet)
library(sf)
library(leaflet.providers)
library(RColorBrewer)
library(tigris)
```

### Dot Density Plot
```{r}
# education_url <- "https://github.com/dyerlab/ENVS-Lectures/raw/master/data/edu_attainment-shp.zip"
# 
# file <- tempfile(pattern = "edu", tmpdir = ".", fileext = ".zip")
# file
# 
# download.file(education_url, destfile = file)
# unzip(file, exdir = ".")
# 
# 
# points <- st_read("edu_attainment-shp")
```

Convert education levels to factor - this allows us to use `colorFactor()` to create our color palette
```{r}
points$education <- factor(points$education, levels = c("No HS diploma", "HS, no Bachelors", "Bachelors" , "Post-Bachelors"  )) 
```

Create color palette
```{r}
factpal <- colorFactor(brewer.pal(n = 4, name = "RdBu"), points$education) #used to color categorical data
```


Create dot density plot
```{r}
leaflet(points) %>% 
  addProviderTiles(providers$CartoDB.Positron)%>% #add provider tiles
  addCircles(stroke = FALSE,  fillOpacity = 1, #add points
              color = ~factpal(education)) %>% #color based on educational attainment
  addLegend(pal = factpal, values = ~education, title = "Education Attainment")%>% #add legend
  addScaleBar(position = "bottomright") #add scale bar
```

### Choropleth plot

load in the census tract data
```{r}
tracts <- tracts("VA", "Richmond city") %>%
  st_transform(4326)
```

Data Prep
```{r message=FALSE, warning=FALSE}
edu_tracts <- tracts %>%
  st_buffer(dist = 0) %>% # buffer by 0 to avoid error message
  st_intersection(points) # intersect the point data with the tract polygons
```

```{r message=FALSE, warning=FALSE}
edu_tracts %>%
  group_by(NAME, education) %>% # group by tract name and education level
  tally() -> tally # tally() counts the number of rows for the groups, so output is one row for every attainment level for each tract (so if a tract has all 4 attainment levels, there are 4 rows of data for that tract)

```

```{r}
tally <- as.data.frame(tally) # converted to data.frame to be able to drop the geometry because the `pivot_wider()` function would not work the way I wanted it to when the geometry was present

tally <- tally %>%
  select(NAME, education, n)%>% # do not want the geometry 
  pivot_wider(names_from = education, values_from = n) # pivot the table so that instead of having multiple rows per tract, you have one row per tract with a column for each attainment level

tally[is.na(tally)] <- 0 # define NA values as zero

tally
```

Create attainment metric for choropleth

```{r}
tally <- tally %>%
  mutate(edu = (((`No HS diploma` * 1) + (`HS, no Bachelors` * 2) + (`Bachelors` * 3) + (`Post-Bachelors` * 4))/
                  (`No HS diploma`  + `HS, no Bachelors` + `Bachelors` + `Post-Bachelors`)))

# I created a column, edu, and did a weighted average for each row. So 'No HS diploma' was weighted as 1, 'HS, no Bachelors' as 2, ect. 

# I used this new column as my average education attainment value to color my polygons
```

```{r}
tally %>%
  select(NAME, edu) -> tally # no longer need the individual count data
```

Join the education data back with the tract data

```{r}
tracts %>%
  left_join(tally, by = "NAME") -> tracts
```

Create the palette

```{r}
pal <- colorNumeric(
  palette = "RdBu",
  domain = tracts$edu) # This time I want a continuos palatte, so I used `colorNumeric()`
```

Make the choropleth map

```{r}
leaflet(tracts) %>%
  addProviderTiles(providers$CartoDB.Positron)%>% # add provider tiles
  addPolygons( fillOpacity = 1, # add tract polygons
               fillColor = ~pal(edu), smoothFactor = 0.2, # fill color based on the calculated 'edu' metric
               color = "grey", 
               opacity = 1,
               weight = 1) %>%
  addLegend(pal = pal, values = ~edu, title = "Education Attainment") # add legend
```

I did not like this legend, as it's unclear what the numbers mean. With some googling I found a solution

```{r}
labels <-c("No HS diploma (1)", "HS, no Bachelors (2)",
           "Bachelors (3)" , "Post-Bachelors (4)"  ) # define the labels for the legend
```

```{r}
leaflet(tracts) %>%
  addProviderTiles(providers$CartoDB.Positron)%>% # add provider tiles
  addPolygons( fillOpacity = 1, # add tract polygons
               fillColor = ~pal(edu), smoothFactor = 0.2, # fill color based on the calculated 'edu' metric
               color = "grey", 
               opacity = 1,
               weight = 1) %>%
  addLegend(pal = pal, values = ~edu, 
            opacity = 1,
            title = "Education Attainment",
            labFormat = function(type, cuts, p) {  # Here's the trick
              paste0(labels)
            })
```
That looks better, I think

### Combine the maps

```{r}

leaflet() %>% # I decided to define by data by layer because I have multiple data sets
  addProviderTiles(providers$CartoDB.Positron) %>% # Add provider tiles
  addPolygons(data = tracts, # I decided to plot the tracts on the dot density map
              color = "black",
              weight = 1,
              fillOpacity = 0,
              group = "Circle" # Need to groups, 1 for density plot and 1 for choropleth
              )  %>%
  addCircles(data = points,
             stroke = FALSE,  fillOpacity = 1, #plot the circles on top of the tracts
             color = ~factpal(education),
             group = "Circle" # Define group
             ) %>%
  addLegend(data = points,
            pal = factpal, # Add legend for dot density plot
            values = ~education,
            opacity = 1,
            title = "Educational Attainment",
            group = "Circle" # Define group for legend
            ) %>%
  groupOptions("Circle", zoomLevels = 1:12 # Define the zoom levels for the dot density plot (dots will only by visible between zoom levels 1 and 12)
               ) %>%
  addPolygons( data = tracts, # First layer of the choropleth map, tract polygons
               fillOpacity = .75,
               fillColor = ~pal(edu), smoothFactor = 0.2, # Color defined by attainment metric
               color = "grey",
               opacity = 1,
               weight = 1,
               label = ~paste("Attainment:", round(edu, digits = 2)), # Adding a label with the attainment metric, rounding to 2 digits after the decimal
               group = "Choro" # Called my second group "Choro"
               ) %>%
  addLegend(data = tracts,
            pal = pal, # Add legend for choropleth plot
            values = ~edu,
            opacity = .75,
            title = "Educational Attainment",
            labFormat = function(type, cuts, p) {  # Here's the trick again
              paste0(labels)
            },
            group = "Choro" # Add to choropleth group
            ) %>%
 groupOptions("Choro", zoomLevels = 13:20  # Define levels for the choropleth map (choropleth polygons will only be visble between zoom levls 13 and 20)
              ) %>%
  addScaleBar(position = "bottomright") # Add scale bar. Scale bar does not need a group because I want it visible at all zoom levels
 


```

Final map


























